How to Run Self-Hosted LLMs on Kubernetes (oneuptime.com)

A new comprehensive guide detailing how to run self-hosted large language models (LLMs) on Kubernetes has been released, emphasizing their benefits such as enhanced data privacy, cost control, reduced latency, and customization. The guide outlines the entire deployment process, from setting up GPU access in Kubernetes to deploying a high-throughput serving engine like vLLM. This approach allows organizations to maintain sensitive data within their infrastructure while effectively managing resource-intensive workloads using Kubernetes orchestration. This development is significant for the AI/ML community, as it empowers users to leverage powerful LLMs without reliance on external APIs, thereby improving data security and potentially lowering operational costs. The guide includes key technical details, such as the installation of GPU drivers, configuration for efficient memory management with PagedAttention, and deployment best practices, notably the use of PersistentVolumeClaims for model storage. Moreover, the tutorial encourages scalability through Kubernetes Event-driven Autoscaling (KEDA), allowing real-time adjustments based on demand. Overall, this resource enhances the feasibility of utilizing advanced AI models in various applications while optimizing resource utilization.